Our Columbus™ Image Data Storage and Analysis system is an instrument agnostic image analysis and management platform. The Columbus system is the only system that provides universal high-volume image data storage and analysis and brings access to images from a wide range of sources including all major high content screening instruments.
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The Columbus system has powerful image analysis capabilities with highly flexible and easy to use building blocks to analyze simple and complex phenotypes of cells.
Columbus Plus extends the current Columbus image analysis and management capabilities by offering real-time image analysis utilizing cluster based high performance computing (HPC) with Columbus Building Blocks. Columbus Plus is offered either as an on premise installation, which leverages customer’s internal data center, or as a hosted Cloud service by PerkinElmer.
High Content Screening experiments generate massive amounts of image data that needs to be accessed quickly, analyzed and re-analyzed, shared with colleagues and stored safely. With the trend towards using more complex, physiologically relevant disease models, more sophisticated tools are required to numerically describe cells and their phenotypes comprehensively. The Columbus system is the only image data storage and analysis system that supports a wide range of file formats, allowing visualization of images, regardless of their origin.
The segmentation of high resolution images to identify individual cells or regions in tissues followed by state-of-the-art cell quantification (morphology, texture, intensity statistics, etc.) takes hours (single plates) or even weeks or months when using conventional computing systems on large screens. To enable reasonable analysis time even when working with large screens of data, Columbus Plus utilizes HPC systems to solve that problem. HPC refers to the practice of aggregating computing power in a way that delivers much higher performance than one could get out of a typical desktop computer or workstation. In a cluster of many computing nodes, wells from microtiter plate are analyzed in parallel. Hence the total analysis time of multi-well plate (commonly 96, 384 and 1536) scales with the number of computing nodes available. To avoid conflicting access of image files, all individual nodes are connected to a high performance file system.
Columbus Plus supports the most common used job schedulers (SGE and Spark) to distribute the well based image analysis to the available computing nodes. Columbus Plus provides the high performance batch analysis of entire screens with just one mouse click.
One of the key challenges for High Throughput Screening (HTS) is that identified hits in the screens showed a very high failure rate in secondary toxicology screens. Only a few (2-3) parameters of the HCS screen were analyzed. Toxicology parameters were not taken into account for the hit profiling. HCS however promises to provide significantly better quality with biologically relevance results. The more parameters extracted from an image the better the quality if the profiling is done properly (e.g. with PerkinElmer High Content Profiler). With significant reduction in image analysis time from days to hours, leveraging Columbus Plus’ industry leading Building Blocks for image analysis and PhenoLOGIC™ machine learning technology, will enable scientists to collect more cellular features without sacrificing or delaying project timelines, therefore increasing the data quality for profiling analysis.
|Version Number||Real-time image analysis utilizing cluster based high performance computing (HPC) with Columbus Building Blocks.|
This study illustrates the power of high content automated image analysis to quantify complex organelle morphologies, using the Opera system and the texture and STAR features of the Columbus system.
There has been a lot of buzz around artificial intelligence, machine learning and deep learning. Is the reality living up to the hype?
In the world of cellular imaging and its application to drug discovery, there is evidence of real progress against some of the critical challenges facing scientists using these technologies.
In this white paper, you will learn about: